Magnetic Resonance Imaging (MRI) equipment allow the capture of sequences of digital images containing three dimensional (3D) human body structures. The computerized visualization and analysis of such structures have revolutionized the medical practice in many ways. This work focuses on the human brain analysis based on MRI images.A healthy brain presents a high symmetry degree with respect to the sagittal plane, that divides it in two parts, the left and right hemispheres. An asymmetry at this plane can, therefore, be a symptom of a disease, such as epilepsy, Alzheimer's or brain tumor [2]. Volume abnormalities in certain structures and cavities, such as the lateral ventricles, can also be associated to diseases, such as schizophrenia, depression and dementia [1].This work is focused on the development of asymmetry measures of the lateral ventricles, whose analysis in both controls and patients can contribute to the study of brain diseases. This analysis is split into three basic steps: the lateral ventricles segmentation, the feature extraction from the segmented structures and the data classification and analysis according to the extracted features. For the lateral ventricles segmentation, many techniques available in the literature were studied, for both manual and automatic approaches. However, there are very few references available in the literature focusing on lateral ventricles segmentation. This work is, in this sense, pioneer, since it presents techniques for lateral ventricles segmentation that allow very limited user intervention, reducing the time spent in the task. Two different approaches were used to extract the features from the lateral ventricles: Multiscale Fractal Dimension and Image Registration. Additionally, for the feature extraction process, we had to implement a technique for the localization and alignment of the mid-sagittal plane of the brain, in order to correct a typical problem in the MRI capturing procedure -the misalignment of the head with respect to the sagittal plane of the image. This technique is a direct contribution of this work. Finally, in the last step of the process -the classification task -two techniques were used, one manual and another automatic, in order to compare the efficiency and effectiveness between them. The manual classification was based in 2D and 3D image analysis, while the automatic classification was based on the Optimum Path Forest (OPF), a technique developed inside the Institute of Computing at Unicamp. The classification results were ix analysed through many confusion matrices, generated from the data obtained from the manual and automatic classifications. Those analyses compare the efficiency of the many classification approaches used in this work, pointing the advantages and disadvantages in each of them.This work is part of the FAPESP thematic project no. 03/13424-1 and is also related to the FAPESP CInAPCe (Inter-institutional Cooperation to Support Brain Research) thematic project, that involves researchers from many institutions, specially f...